Using LaunchDarkly with Knock to A/B test messaging
Learn how to use LaunchDarkly with Knock to A/B test messaging workflows and templates.
In this tutorial we'll walk through how to use LaunchDarkly segments and experiments to power A/B testing across Knock's cross-channel messaging.
Overview#
A/B testing your messaging can improve engagement rates, conversion, and user experience. By testing different message variants, channels, timing, and targeting, you can optimize your notification strategy based on real user behavior data.
This tutorial shows you how to combine LaunchDarkly's feature flags and experimentation platform with Knock's notification infrastructure to run A/B tests on your workflow messaging.
What you'll learn:
- How to use LaunchDarkly flags to control message variants
- How to leverage LaunchDarkly segments for targeted messaging experiments
- How to implement A/B testing logic in Knock workflows using branch steps
- How to measure and analyze messaging experiment results
- Best practices for messaging experimentation
Integration architecture#
The LaunchDarkly + Knock integration follows this flow:
- LaunchDarkly defines your experiment parameters (flags, segments, variations.)
- Your application evaluates flags and passes results to Knock via trigger data.
- Knock workflows use branch steps to deliver different messaging experiences.
- Knock tracking captures engagement metrics (opens, clicks, conversions.)
- Knock warehouse extension combines Knock engagement data with LaunchDarkly experiment results.
With Knock and LaunchDarkly you can test message content, channels, and timing, target specific user segments with different strategies, measure the impact of messaging changes on business metrics, and make data-driven decisions about your notification strategy.
LaunchDarkly concepts and setup#
Flags#
LaunchDarkly Flags serve as the control mechanism for your messaging experiments. They determine which message variant each user receives.
We'll be using the example flag below in our tutorial. You could also create multi-variant flags to run more complex experiments with different message content, channels, and timing.
Simple A/B test flag:
- Key:
messaging-AB-test
- Variations:
control
,treatment
- Default:
control
Segments#
LaunchDarkly Segments enable you to define test populations and ensure consistent user experiences across multiple flags.
Experiments#
LaunchDarkly experiments connect your messaging flags to business metrics, enabling you to measure the effectiveness of different messaging strategies.
Using LaunchDarkly with Knock workflows#
You can use LaunchDarkly with Knock workflows to orchestrate different cross-channel messaging experiences based on the flags and experiments you've defined in LaunchDarkly. We'll cover two methods for using LaunchDarkly with Knock workflows: flag-controlled workflow selection and branch-based message variants.
Method 1: Flag-controlled workflow selection#
This approach uses LaunchDarkly flags to determine which Knock workflow to trigger, enabling you to test different notification strategies. You can use this approach to test different messaging approaches, compare email vs. SMS vs. push notification strategies, or test different workflow timing and sequences.
Method 2: Branch-based message variants#
This approach uses a single workflow with branch steps to deliver different messaging experiences based on LaunchDarkly data passed via trigger data. You can use this approach to A/B test different templates within the same workflow, personalize message content based on user segments, or run other kinds of flag-based experiments.
Analytics and analyzing results#
Knock automatically tracks engagement metrics across all channels, providing the data you need to measure experiment success. Learn more about Knock engagement status and link/open tracking.
You can evaluate this data in Knock's dashboard, or bring it into your stack for analysis using Knock's warehouse extension or outbound webhooks.
Implementation example: welcome email A/B test#
This example shows a complete implementation of A/B testing welcome emails with different messaging styles.